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GEOSTATS: ANN: Subsurface Simulation/Characterization Paper available on web

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  • Upmanu Lall
    Please check the link http://pub.uwrl.usu.edu/~ulall/knn-drill/ to view the paper indicated below. it has been submitted to Water Resources Research A
    Message 1 of 1 , May 8, 1998
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      Please check the link http://pub.uwrl.usu.edu/~ulall/knn-drill/
      to view the paper indicated below. it has been submitted to Water Resources
      Research


      A k-Nearest Neighbor Bootstrap Simulator of Pseudo-Bore Hole Logs for
      Subsurface
      Characterization

      Upmanu Lall and Alaa Ibrahim Ali
      Utah Water Research Lab. and Dept. of Civil and Environmental Eng.,
      Utah State University, Logan UT 84322-8200
      ABSTRACT

      Subsurface characterization is important for investigations of groundwater
      contamination as well as for petroleum extraction potential. Bore hole
      logs are a
      common source of localized stratigraphic information. Sampling and
      interpolation uncertainties complicate the characterization of the
      subsurface from the data.
      Geostatistical methods are often used to map subsurface attributes from
      such data, and to generate conditional simulations that honor observed
      strata. The existing
      methods often require an a priori discretization of the bore hole data
      along each vertical section into strata types, as well as assumptions as to
      the statistical
      homogeneity of the underlying random field that generated the discretized
      data.
      A method for generating likely realizations of subsurface soils by
      resampling pseudo-bore hole logs on to a horizontal grid is presented
      here. Entire bore hole logs
      are resampled on to the grid locations, thus obviating the need for a prior
      discretization of the bore hole data. The bootstrapping algorithm used is
      motivated by a
      nonhomogeneous random field description of the data with the necessary
      probability distributions defined implicitly through a nonparametric
      (k-nearest neighbor)
      probability density estimate for the sampling distribution of the bore hole
      logs (real or pseudo) that lie in a neighborhood of a grid point at which
      resampling is
      needed. Example applications to a synthetic and to a real data set are
      provided. Extensions to incorporate other sources of information, as well
      as potential
      applications of the method, are discussed.

      ___________________________________________________________________
      Upmanu Lall
      Professor, Civil and Environmental Engineering
      Associate Director, Utah Water Research Laboratory
      Utah State University
      Logan UT 84322-8200
      ___________________________________________________________________
      e-mail: ulall@...
      Web: http://grumpy.usu.edu/~FALALL/ulall.html
      http://publish.uwrl.usu.edu/faculty/lall.html
      ___________________________________________________________________
      Phone: 801-797-3184 FAX: 801-797-3663
      ___________________________________________________________________


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